nitinmukesh
commented
2 months ago So it's 3 hours and still progress is 0.
Closed nitinmukesh closed 1 month ago
nitinmukesh
commented
2 months ago So it's 3 hours and still progress is 0.
itechmusic
commented
2 months ago I tried and it work for me.
After I resized the image from 667 x 741 to 666 x 740, I got this result
https://github.com/TMElyralab/MuseTalk/assets/163980830/a7ca806b-6302-4893-a007-14892039fbbd
nitinmukesh
commented
2 months ago Thank you @itechmusic.
I am still not sure how to make it work. I did adjusted the dimension of image as suggested by you.
There is no error, no missing package so don't know what should I try or where to look. VRAM is also available.
In results folder
Path is also correct
(venv) C:\sd\MuseTalk>pip list
Package Version
---------------------------- ------------
absl-py 2.1.0
accelerate 0.28.0
addict 2.4.0
aiofiles 23.2.1
aliyun-python-sdk-core 2.15.1
aliyun-python-sdk-kms 2.16.2
altair 5.3.0
annotated-types 0.6.0
antlr4-python3-runtime 4.9.3
anyio 4.3.0
astunparse 1.6.3
attrs 23.2.0
beautifulsoup4 4.12.3
cachetools 5.3.3
certifi 2024.2.2
cffi 1.16.0
charset-normalizer 3.3.2
chumpy 0.70
click 8.1.7
colorama 0.4.6
contourpy 1.2.1
crcmod 1.7
cryptography 42.0.5
cycler 0.12.1
Cython 3.0.10
decorator 4.4.2
diffusers 0.27.2
exceptiongroup 1.2.1
fastapi 0.110.2
ffmpeg-python 0.2.0
ffmpy 0.3.2
filelock 3.13.4
flatbuffers 24.3.25
fonttools 4.51.0
fsspec 2024.3.1
future 1.0.0
gast 0.4.0
gdown 5.1.0
google-auth 2.29.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
gradio 4.27.0
gradio_client 0.15.1
grpcio 1.62.2
h11 0.14.0
h5py 3.11.0
httpcore 1.0.5
httpx 0.27.0
huggingface-hub 0.22.2
idna 3.7
imageio 2.34.1
imageio-ffmpeg 0.4.9
importlib_metadata 7.1.0
importlib_resources 6.4.0
jax 0.4.26
Jinja2 3.1.3
jmespath 0.10.0
json-tricks 3.17.3
jsonschema 4.21.1
jsonschema-specifications 2023.12.1
keras 2.12.0
kiwisolver 1.4.5
libclang 18.1.1
Markdown 3.6
markdown-it-py 3.0.0
MarkupSafe 2.1.5
matplotlib 3.8.4
mdurl 0.1.2
ml-dtypes 0.4.0
mmcv 2.1.0
mmdet 3.2.0
mmengine 0.10.4
mmpose 1.3.1
model-index 0.1.11
moviepy 1.0.3
mpmath 1.3.0
munkres 1.1.4
networkx 3.3
numpy 1.23.5
oauthlib 3.2.2
omegaconf 2.3.0
opencv-python 4.9.0.80
opendatalab 0.0.10
openmim 0.3.9
openxlab 0.0.38
opt-einsum 3.3.0
ordered-set 4.1.0
orjson 3.10.1
oss2 2.17.0
packaging 24.0
pandas 2.2.2
pillow 10.3.0
pip 22.2.1
platformdirs 4.2.0
proglog 0.1.10
protobuf 4.25.3
psutil 5.9.8
pyasn1 0.6.0
pyasn1_modules 0.4.0
pycocotools 2.0.7
pycparser 2.22
pycryptodome 3.20.0
pydantic 2.7.0
pydantic_core 2.18.1
pydub 0.25.1
Pygments 2.17.2
pyparsing 3.1.2
PySocks 1.7.1
python-dateutil 2.9.0.post0
python-multipart 0.0.9
pytz 2023.4
pywin32 306
PyYAML 6.0.1
referencing 0.34.0
regex 2024.4.16
requests 2.28.2
requests-oauthlib 2.0.0
rich 13.4.2
rpds-py 0.18.0
rsa 4.9
ruff 0.4.1
safetensors 0.4.3
scipy 1.13.0
semantic-version 2.10.0
setuptools 60.2.0
shapely 2.0.4
shellingham 1.5.4
six 1.16.0
sniffio 1.3.1
soundfile 0.12.1
soupsieve 2.5
spaces 0.26.1
starlette 0.37.2
static-ffmpeg 2.5
sympy 1.12
tabulate 0.9.0
tensorboard 2.12.0
tensorboard-data-server 0.7.2
tensorboard-plugin-wit 1.8.1
tensorflow 2.12.0
tensorflow-estimator 2.12.0
tensorflow-intel 2.12.0
tensorflow-io-gcs-filesystem 0.31.0
termcolor 2.4.0
terminaltables 3.1.10
tokenizers 0.15.2
tomli 2.0.1
tomlkit 0.12.0
toolz 0.12.1
torch 2.0.1+cu118
torchaudio 2.0.2+cu118
torchvision 0.15.2+cu118
tqdm 4.65.2
transformers 4.39.2
typer 0.12.3
typing_extensions 4.11.0
tzdata 2024.1
urllib3 1.26.18
uvicorn 0.29.0
websockets 11.0.3
Werkzeug 3.0.2
wheel 0.43.0
wrapt 1.14.1
xtcocotools 1.14.3
yapf 0.40.2
zipp 3.18.1It is getting stuck at the following line (recon = vae.decode_latents(pred_latents))
@torch.no_grad() def main(args):
print("start inference")
video_num = len(whisper_chunks)
print("video_num ", video_num)
batch_size = args.batch_size
print("batch_size ", batch_size)
gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size)
print("gen ", gen)
res_frame_list = []
for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))):
print("whisper_batch ", whisper_batch)
print("latent_batch ", latent_batch)
print("i ", i)
tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch]
print("tensor_list ", tensor_list)
audio_feature_batch = torch.stack(tensor_list).to(unet.device) # torch, B, 5*N,384
print("audio_feature_batch ", audio_feature_batch)
audio_feature_batch = pe(audio_feature_batch)
print("audio_feature_batch ", audio_feature_batch)
pred_latents = unet.model(latent_batch, timesteps, encoder_hidden_states=audio_feature_batch).sample
print("pred_latents ", pred_latents)
recon = vae.decode_latents(pred_latents)
print("recon ", recon)
for res_frame in recon:
print("res_frame ", res_frame)
res_frame_list.append(res_frame)(venv) C:\sd\MuseTalk>python -m scripts.inference --inference_config configs/inference/test.yaml
add ffmpeg to path
Loads checkpoint by local backend from path: ./models/dwpose/dw-ll_ucoco_384.pth
cuda start
{'task_0': {'video_path': 'data/image/face.jpeg', 'audio_path': 'data/audio/audio.wav'}}
video in 25 FPS, audio idx in 50FPS
extracting landmarks...time consuming
reading images...
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 95.58it/s]
get key_landmark and face bounding boxes with the default value
100%|████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.89s/it]
********************************************bbox_shift parameter adjustment**********************************************************
Total frame:「1」 Manually adjust range : [ -25~23 ] , the current value: 0
*************************************************************************************************************************************
start inference
video_num 24
batch_size 8
gen <generator object datagen at 0x000001A7FE10FB50>
0%| | 0/3 [00:00<?, ?it/s]whisper_batch [[[-1.6992e-01 -2.8849e-04 -1.4514e-01 ... 9.9609e-01 9.5850e-01
8.3105e-01]
[ 6.5479e-01 6.7578e-01 2.2339e-01 ... 6.2012e-01 8.7939e-01
7.4072e-01]
[-1.8799e-02 1.9385e-01 2.3035e-01 ... 6.1963e-01 1.0977e+00
8.1250e-01]
...
[ 8.7891e-01 6.8164e-01 2.3022e-01 ... 5.9766e-01 7.5195e-01
3.6182e-01]
[-3.9062e-02 6.7188e-01 2.3315e-01 ... 9.6973e-01 1.5469e+00
1.0498e+00]
[ 2.8564e-02 9.0479e-01 1.8616e-01 ... 2.5156e+00 2.3164e+00
1.1719e+00]]
[[-1.6992e-01 -2.8849e-04 -1.4514e-01 ... 9.9609e-01 9.5850e-01
8.3105e-01]
[ 6.5479e-01 6.7578e-01 2.2339e-01 ... 6.2012e-01 8.7939e-01
7.4072e-01]
[-1.8799e-02 1.9385e-01 2.3035e-01 ... 6.1963e-01 1.0977e+00
8.1250e-01]
...
[ 5.3662e-01 8.0469e-01 8.2959e-01 ... 6.2012e-01 9.1797e-01
6.6113e-01]
[ 5.0903e-02 6.9922e-01 6.6895e-01 ... 3.8623e-01 1.7100e+00
7.8516e-01]
[ 1.0413e-01 4.3945e-02 1.3086e+00 ... 1.6094e+00 2.7227e+00
3.5864e-01]]
[[-1.6992e-01 -2.8849e-04 -1.4514e-01 ... 9.9609e-01 9.5850e-01
8.3105e-01]
[ 6.5479e-01 6.7578e-01 2.2339e-01 ... 6.2012e-01 8.7939e-01
7.4072e-01]
[-1.8799e-02 1.9385e-01 2.3035e-01 ... 6.1963e-01 1.0977e+00
8.1250e-01]
...
[ 1.6875e+00 1.5293e+00 9.4727e-01 ... 5.1660e-01 8.5889e-01
5.2393e-01]
[ 1.8154e+00 1.4775e+00 1.2705e+00 ... 1.1113e+00 1.0674e+00
9.5215e-01]
[ 1.2295e+00 2.2734e+00 3.2852e+00 ... 2.1562e+00 2.4258e+00
1.3252e+00]]
...
[[-4.4922e-01 -5.3613e-01 -8.9355e-01 ... 8.7598e-01 8.7891e-01
8.3105e-01]
[ 5.0098e-01 7.6953e-01 5.7764e-01 ... 6.0791e-01 5.8838e-01
6.2305e-01]
[ 2.5928e-01 5.1709e-01 8.2031e-01 ... 4.8926e-01 9.0381e-01
6.9824e-01]
...
[ 8.1689e-01 1.3691e+00 1.0293e+00 ... 3.8672e-01 9.8975e-01
8.1836e-01]
[ 3.6963e-01 1.7598e+00 9.6191e-01 ... 6.6504e-01 1.5371e+00
9.3359e-01]
[ 2.8662e-01 4.6250e+00 1.6562e+00 ... 4.1284e-01 1.7520e+00
1.6016e+00]]
[[ 8.4326e-01 9.7363e-01 6.6113e-01 ... 8.5986e-01 8.7842e-01
8.5547e-01]
[ 1.3496e+00 1.3945e+00 8.2373e-01 ... 4.4141e-01 6.6602e-01
5.6982e-01]
[ 1.2510e+00 1.2715e+00 1.1982e+00 ... 5.4297e-01 8.2275e-01
5.5371e-01]
...
[ 8.1104e-01 1.8994e+00 6.9092e-01 ... 4.3701e-01 6.8604e-01
8.3252e-01]
[-3.7793e-01 2.3457e+00 9.8096e-01 ... 7.3438e-01 1.0410e+00
9.4482e-01]
[-1.2832e+00 4.2266e+00 1.4570e+00 ... 2.8198e-01 5.7861e-01
1.2695e+00]]
[[-6.7480e-01 -1.0455e-01 1.6760e-01 ... 8.3008e-01 8.7988e-01
8.3496e-01]
[ 1.6562e+00 1.4619e+00 3.9160e-01 ... 4.4092e-01 9.5801e-01
7.1045e-01]
[ 1.5234e+00 1.3545e+00 5.2588e-01 ... 3.2446e-01 1.0654e+00
5.7080e-01]
...
[ 1.0566e+00 1.6387e+00 3.7231e-01 ... 7.9590e-01 7.4268e-01
1.0000e+00]
[-2.1680e-01 2.4570e+00 4.3555e-01 ... 5.1758e-01 1.0869e+00
9.8877e-01]
[-2.0703e+00 3.5664e+00 1.5537e+00 ... 1.5454e-01 8.2764e-01
1.5273e+00]]]
latent_batch tensor([[[[ 0.3817, -0.9511, -0.0135, ..., 0.3239, -0.3727, -1.2085],
[-0.7161, 0.1602, -0.8396, ..., -0.7383, -0.4438, -0.2077],
[-1.0292, -0.2511, -1.4369, ..., -0.2085, -0.2418, -1.1311],
...,
[-1.2580, -1.0452, -0.9995, ..., -0.9927, -1.1169, -1.3844],
[-1.6654, -1.2902, -1.1895, ..., -1.1970, -1.2997, -1.6015],
[-1.1598, -1.9114, -1.4038, ..., -1.7775, -1.6023, -1.4959]],
[[-0.1305, -0.9890, -0.2282, ..., -0.3648, -0.6344, -0.3028],
[-1.2666, -0.5069, -0.5656, ..., -1.1775, -0.1794, -0.7154],
[-1.3254, -0.2052, -1.7900, ..., -0.6103, -1.5310, -0.8676],
...,
[-2.6156, -2.6934, -2.6734, ..., -2.7262, -2.6924, -2.6582],
[-2.6062, -2.6643, -2.6643, ..., -2.7230, -2.7091, -2.6203],
[-2.5358, -2.6483, -2.5463, ..., -2.7173, -2.6786, -2.5009]],
[[ 0.9009, 0.2777, -0.9097, ..., 0.9195, -0.0183, -0.5688],
[ 0.4648, -0.2058, 0.8519, ..., -0.3059, -0.1156, 0.8472],
[-0.6064, -0.1322, -0.6453, ..., 0.9855, 0.5003, -0.4622],
...,
[ 0.8297, 1.1355, 1.1744, ..., 1.1504, 1.2261, 1.4360],
[ 0.7979, 1.2265, 1.2789, ..., 1.2295, 1.2898, 1.5244],
[ 1.2797, 1.2329, 1.4728, ..., 1.2937, 1.4724, 1.4008]],
...,
[[-0.0796, -1.0523, -0.1950, ..., -0.4383, -0.7383, -0.2432],
[-1.2808, -0.4969, -0.4962, ..., -1.1745, -0.1237, -0.7957],
[-1.3536, -0.1554, -1.8771, ..., -0.5685, -1.5523, -0.9214],
...,
[-1.9274, -1.0349, -1.3094, ..., -0.3054, -1.3915, -1.3486],
[-0.7738, -0.7489, -1.1319, ..., -1.9757, -0.8756, -2.6383],
[-1.8903, -0.6943, -1.2970, ..., -0.3808, -1.3761, -1.0375]],
[[ 1.0789, 0.2526, -0.9007, ..., 0.8455, 0.0512, -0.5581],
[ 0.5242, -0.2375, 0.9145, ..., -0.3762, -0.2012, 0.9488],
[-0.7725, -0.1789, -0.9380, ..., 0.9561, 0.3656, -0.5224],
...,
[ 0.6917, -0.3562, -0.2107, ..., 0.4791, -0.9471, 0.6350],
[ 1.5163, 0.0446, 0.5912, ..., -0.0412, 0.0447, -1.1374],
[ 1.5193, -1.0238, -0.1816, ..., 0.7875, -0.0535, 0.2286]],
[[ 0.9593, 0.6785, 0.1850, ..., 1.3929, -0.4940, -0.7949],
[ 0.5678, 0.9684, 0.1273, ..., -0.9839, 0.8837, 0.8088],
[ 0.2073, -0.0170, 0.0418, ..., 1.2770, 0.3857, -0.4266],
...,
[-0.0539, 0.4435, 1.6115, ..., 0.5722, 0.2167, 1.0428],
[ 1.5132, 0.8811, 1.9802, ..., 1.3308, 0.2180, 0.2373],
[ 1.0616, -0.4796, 1.1155, ..., 0.8659, 1.5309, 0.1066]]],
[[[ 0.3817, -0.9511, -0.0135, ..., 0.3239, -0.3727, -1.2085],
[-0.7161, 0.1602, -0.8396, ..., -0.7383, -0.4438, -0.2077],
[-1.0292, -0.2511, -1.4369, ..., -0.2085, -0.2418, -1.1311],
...,
[-1.2580, -1.0452, -0.9995, ..., -0.9927, -1.1169, -1.3844],
[-1.6654, -1.2902, -1.1895, ..., -1.1970, -1.2997, -1.6015],
[-1.1598, -1.9114, -1.4038, ..., -1.7775, -1.6023, -1.4959]],
[[-0.1305, -0.9890, -0.2282, ..., -0.3648, -0.6344, -0.3028],
[-1.2666, -0.5069, -0.5656, ..., -1.1775, -0.1794, -0.7154],
[-1.3254, -0.2052, -1.7900, ..., -0.6103, -1.5310, -0.8676],
...,
[-2.6156, -2.6934, -2.6734, ..., -2.7262, -2.6924, -2.6582],
[-2.6062, -2.6643, -2.6643, ..., -2.7230, -2.7091, -2.6203],
[-2.5358, -2.6483, -2.5463, ..., -2.7173, -2.6786, -2.5009]],
[[ 0.9009, 0.2777, -0.9097, ..., 0.9195, -0.0183, -0.5688],
[ 0.4648, -0.2058, 0.8519, ..., -0.3059, -0.1156, 0.8472],
[-0.6064, -0.1322, -0.6453, ..., 0.9855, 0.5003, -0.4622],
...,
[ 0.8297, 1.1355, 1.1744, ..., 1.1504, 1.2261, 1.4360],
[ 0.7979, 1.2265, 1.2789, ..., 1.2295, 1.2898, 1.5244],
[ 1.2797, 1.2329, 1.4728, ..., 1.2937, 1.4724, 1.4008]],
...,
[[-0.0796, -1.0523, -0.1950, ..., -0.4383, -0.7383, -0.2432],
[-1.2808, -0.4969, -0.4962, ..., -1.1745, -0.1237, -0.7957],
[-1.3536, -0.1554, -1.8771, ..., -0.5685, -1.5523, -0.9214],
...,
[-1.9274, -1.0349, -1.3094, ..., -0.3054, -1.3915, -1.3486],
[-0.7738, -0.7489, -1.1319, ..., -1.9757, -0.8756, -2.6383],
[-1.8903, -0.6943, -1.2970, ..., -0.3808, -1.3761, -1.0375]],
[[ 1.0789, 0.2526, -0.9007, ..., 0.8455, 0.0512, -0.5581],
[ 0.5242, -0.2375, 0.9145, ..., -0.3762, -0.2012, 0.9488],
[-0.7725, -0.1789, -0.9380, ..., 0.9561, 0.3656, -0.5224],
...,
[ 0.6917, -0.3562, -0.2107, ..., 0.4791, -0.9471, 0.6350],
[ 1.5163, 0.0446, 0.5912, ..., -0.0412, 0.0447, -1.1374],
[ 1.5193, -1.0238, -0.1816, ..., 0.7875, -0.0535, 0.2286]],
[[ 0.9593, 0.6785, 0.1850, ..., 1.3929, -0.4940, -0.7949],
[ 0.5678, 0.9684, 0.1273, ..., -0.9839, 0.8837, 0.8088],
[ 0.2073, -0.0170, 0.0418, ..., 1.2770, 0.3857, -0.4266],
...,
[-0.0539, 0.4435, 1.6115, ..., 0.5722, 0.2167, 1.0428],
[ 1.5132, 0.8811, 1.9802, ..., 1.3308, 0.2180, 0.2373],
[ 1.0616, -0.4796, 1.1155, ..., 0.8659, 1.5309, 0.1066]]],
[[[ 0.3817, -0.9511, -0.0135, ..., 0.3239, -0.3727, -1.2085],
[-0.7161, 0.1602, -0.8396, ..., -0.7383, -0.4438, -0.2077],
[-1.0292, -0.2511, -1.4369, ..., -0.2085, -0.2418, -1.1311],
...,
[-1.2580, -1.0452, -0.9995, ..., -0.9927, -1.1169, -1.3844],
[-1.6654, -1.2902, -1.1895, ..., -1.1970, -1.2997, -1.6015],
[-1.1598, -1.9114, -1.4038, ..., -1.7775, -1.6023, -1.4959]],
[[-0.1305, -0.9890, -0.2282, ..., -0.3648, -0.6344, -0.3028],
[-1.2666, -0.5069, -0.5656, ..., -1.1775, -0.1794, -0.7154],
[-1.3254, -0.2052, -1.7900, ..., -0.6103, -1.5310, -0.8676],
...,
[-2.6156, -2.6934, -2.6734, ..., -2.7262, -2.6924, -2.6582],
[-2.6062, -2.6643, -2.6643, ..., -2.7230, -2.7091, -2.6203],
[-2.5358, -2.6483, -2.5463, ..., -2.7173, -2.6786, -2.5009]],
[[ 0.9009, 0.2777, -0.9097, ..., 0.9195, -0.0183, -0.5688],
[ 0.4648, -0.2058, 0.8519, ..., -0.3059, -0.1156, 0.8472],
[-0.6064, -0.1322, -0.6453, ..., 0.9855, 0.5003, -0.4622],
...,
[ 0.8297, 1.1355, 1.1744, ..., 1.1504, 1.2261, 1.4360],
[ 0.7979, 1.2265, 1.2789, ..., 1.2295, 1.2898, 1.5244],
[ 1.2797, 1.2329, 1.4728, ..., 1.2937, 1.4724, 1.4008]],
...,
[[-0.0796, -1.0523, -0.1950, ..., -0.4383, -0.7383, -0.2432],
[-1.2808, -0.4969, -0.4962, ..., -1.1745, -0.1237, -0.7957],
[-1.3536, -0.1554, -1.8771, ..., -0.5685, -1.5523, -0.9214],
...,
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device='cuda:0')
i 0
tensor_list [tensor([[-1.6992e-01, -2.8849e-04, -1.4514e-01, ..., 9.9609e-01,
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audio_feature_batch tensor([[[-1.6992e-01, -2.8849e-04, -1.4514e-01, ..., 9.9609e-01,
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...,
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6.8604e-01, 8.3252e-01],
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1.0410e+00, 9.4482e-01],
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9.5801e-01, 7.1045e-01],
[ 1.5234e+00, 1.3545e+00, 5.2588e-01, ..., 3.2446e-01,
1.0654e+00, 5.7080e-01],
...,
[ 1.0566e+00, 1.6387e+00, 3.7231e-01, ..., 7.9590e-01,
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[-2.0703e+00, 3.5664e+00, 1.5537e+00, ..., 1.5454e-01,
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audio_feature_batch tensor([[[-0.1699, 0.9997, -0.1451, ..., 1.9961, 0.9585, 1.8311],
[ 1.4963, 1.2161, 1.0386, ..., 1.6201, 0.8795, 1.7407],
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...,
[ 0.6602, -0.1876, 1.5582, ..., 1.6201, 0.9229, 1.6611],
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...,
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...,
[[-0.4492, 0.4639, -0.8936, ..., 1.8760, 0.8789, 1.8311],
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...,
[ 0.9405, 0.3768, 1.7579, ..., 1.3867, 0.9947, 1.8183],
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[-0.6671, 4.9256, 2.0631, ..., 1.4128, 1.7571, 2.6015]],
[[ 0.8433, 1.9736, 0.6611, ..., 1.8599, 0.8784, 1.8555],
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...,
[ 0.9346, 0.9071, 1.4195, ..., 1.4370, 0.6910, 1.8325],
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[-2.2370, 4.5272, 1.8638, ..., 1.2820, 0.5838, 2.2695]],
[[-0.6748, 0.8954, 0.1676, ..., 1.8301, 0.8799, 1.8350],
[ 2.4977, 2.0022, 1.2069, ..., 1.4409, 0.9581, 1.7104],
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...,
[ 1.1802, 0.6463, 1.1009, ..., 1.7959, 0.7476, 2.0000],
[-0.9851, 1.8169, 1.4159, ..., 1.5176, 1.0919, 1.9888],
[-3.0241, 3.8670, 1.9605, ..., 1.1545, 0.8328, 2.5273]]],
device='cuda:0')
pred_latents tensor([[[[ 3.7412e-01, -1.0128e+00, -1.1039e-01, ..., 2.3265e-01,
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...,
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[ 8.5260e-01, -4.5738e-01, -3.6743e-01, ..., -9.2667e-01,
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4.0420e-01, -5.4611e-01],
...,
[ 2.7208e-01, 3.6507e-01, 8.3463e-01, ..., 4.0893e-01,
-4.8551e-01, 3.2502e-01],
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4.1194e-01, -1.0300e-01],
[ 4.9015e-01, -4.7795e-01, 9.2759e-01, ..., 7.8037e-01,
1.8870e-01, 1.8663e-01]],
[[ 8.9137e-01, 6.7537e-01, 2.0731e-01, ..., 1.4242e+00,
-6.0848e-01, -9.0874e-01],
[ 5.7204e-01, 9.3216e-01, 1.0244e-01, ..., -9.7021e-01,
8.9468e-01, 8.1622e-01],
[ 2.2283e-01, 3.3323e-02, 1.8434e-01, ..., 1.3443e+00,
4.2978e-01, -4.3566e-01],
...,
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6.7962e-01, 6.2657e-01],
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6.9901e-01, 8.6582e-01],
[-1.1562e-01, 1.2524e+00, 7.8486e-01, ..., 8.9864e-01,
1.2786e+00, 4.5031e-01]]],
[[[ 3.8042e-01, -1.0157e+00, -1.0492e-01, ..., 2.3506e-01,
-4.4954e-01, -1.4108e+00],
[-8.0537e-01, 1.1882e-01, -9.4281e-01, ..., -8.7574e-01,
-5.0227e-01, -2.6616e-01],
[-1.1198e+00, -3.5971e-01, -1.5452e+00, ..., -3.2310e-01,
-3.4905e-01, -1.3497e+00],
...,
[-3.9963e-02, -7.2257e-01, -2.5422e-01, ..., -4.7732e-01,
-1.0690e+00, -1.5482e+00],
[ 2.6502e-02, -9.1952e-01, -3.4455e-01, ..., -7.1310e-01,
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[-2.8392e-01, -4.1943e-01, -3.7324e-01, ..., -1.3746e+00,
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[[-7.4021e-02, -1.0440e+00, -2.1981e-01, ..., -3.4815e-01,
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-1.2859e-01, -7.5787e-01],
[-1.3406e+00, -8.1378e-02, -1.8372e+00, ..., -5.9666e-01,
-1.5736e+00, -9.1573e-01],
...,
[-7.2981e-01, -5.7384e-01, -1.5248e+00, ..., -7.7529e-01,
-1.3764e+00, -1.0389e+00],
[-9.2240e-02, -4.9245e-01, -1.3387e+00, ..., -2.0503e+00,
-1.3154e+00, -2.2657e+00],
[-1.9961e-01, -3.2101e-01, -1.5318e+00, ..., -7.1576e-01,
-1.6716e+00, -1.2167e+00]],
[[ 9.3784e-01, 2.7220e-01, -9.6078e-01, ..., 8.8829e-01,
-3.5619e-02, -6.2022e-01],
[ 4.9234e-01, -3.0177e-01, 8.7965e-01, ..., -3.6167e-01,
-1.1949e-01, 9.3537e-01],
[-7.6492e-01, -1.6762e-01, -8.0711e-01, ..., 1.0213e+00,
4.0193e-01, -5.4646e-01],
...,
[ 5.1700e-01, 3.3006e-01, 8.1116e-01, ..., 4.0618e-01,
-4.8952e-01, 3.1623e-01],
[-1.3833e-01, -9.3273e-01, 8.9023e-01, ..., 2.3928e-02,
4.3912e-01, -1.1734e-01],
[ 3.2406e-01, -6.7952e-01, 8.8508e-01, ..., 7.7547e-01,
1.8584e-01, 2.1956e-01]],
[[ 8.9341e-01, 6.7749e-01, 2.0655e-01, ..., 1.4277e+00,
-6.1087e-01, -9.0753e-01],
[ 5.7274e-01, 9.3312e-01, 1.0202e-01, ..., -9.6845e-01,
8.9981e-01, 8.1548e-01],
[ 2.2241e-01, 3.2400e-02, 1.8548e-01, ..., 1.3454e+00,
4.3240e-01, -4.3663e-01],
...,
[-8.9979e-03, 8.4620e-01, 1.6043e+00, ..., 4.6532e-01,
6.9437e-01, 5.7828e-01],
[-5.3942e-01, 1.8125e-01, 1.5477e+00, ..., 1.0629e+00,
7.1541e-01, 8.7044e-01],
[-3.7573e-01, 1.0569e+00, 7.7045e-01, ..., 8.9445e-01,
1.2792e+00, 4.6282e-01]]]], device='cuda:0')@itechmusic Please could you suggest something here to make it work
itechmusic
commented
2 months ago (recon = vae.decode_latents(pred_latents))
Sorry, we are not familiar with coding on Windows 11. As you identify that the issue occurs at (recon = vae.decode_latents(pred_latents)), maybe you can try decoding pred_latents one by one instead of using batch inference. You may start with setting batch_size=1 at https://github.com/TMElyralab/MuseTalk/blob/main/scripts/inference.py#L143, which make UNet and VAE-decode use batch=1
I am trying with the attached files and even if I wait for 2 hours no progress. Am I doing something wrong
image.zip
test.yaml
FFmpeg is in path